The global UAV Aerial Survey Services market is experiencing robust growth, driven by increasing demand across diverse sectors. Technological advancements in drone technology, offering higher resolution imagery and improved data processing capabilities, are significantly contributing to this expansion. The market's versatility, providing cost-effective and efficient solutions for various applications, further fuels its growth. Specific sectors like construction, agriculture, and energy are key drivers, utilizing UAV surveys for site mapping, precision agriculture, pipeline inspections, and environmental monitoring. While regulatory hurdles and data security concerns present challenges, the market is overcoming these limitations through the development of standardized operating procedures and robust data encryption techniques. Assuming a conservative CAGR of 15% (a reasonable estimate given the rapid technological advancements and increasing adoption rates in this sector), and a 2025 market size of $2 billion, the market is projected to reach approximately $4.2 Billion by 2033. This substantial growth is further fueled by the increasing affordability and accessibility of UAV technology, enabling more businesses to leverage aerial survey services. The segmentation of the UAV Aerial Survey Services market reveals that orthophoto and oblique image services are widely utilized, catering to diverse application needs. Forestry and agriculture are dominant sectors, with construction, power and energy, and oil & gas industries rapidly adopting this technology. Regional analysis highlights strong growth in North America and Asia-Pacific, driven by significant investments in infrastructure development and agricultural modernization. Europe follows closely, spurred by government initiatives promoting sustainable development and environmental monitoring. The competitive landscape includes both established players like Kokusai Kogyo and Zenrin, and emerging specialized companies, indicating a dynamic and competitive market with potential for further consolidation and innovation. The continued development of advanced data analytics capabilities, integrated with UAV imagery, will create new opportunities and drive market expansion.

Two detailed geomorphological maps (1:2000) depicting landscape changes as a result of a glacial lake outburst flood were produced for the 2.1-km-long section of the Zackenberg river, NE Greenland. The maps document the riverscape before the flood (5 August 2017) and immediately after the flood (8 August 2017), illustrating changes to the riverbanks and morphology of the channel. A series of additional maps (1:800) represent case studies of different types of riverbank responses, emphasising the importance of the lateral thermo-erosion and bank collapsing as significant immediate effects of the flood. The average channel width increased from 40.75 m pre-flood to 44.59 m post-flood, whereas the length of active riverbanks decreased from 1729 to 1657 m. The new deposits related to 2017 flood covered 93,702 m2. The developed maps demonstrated the applicability of small Unmanned Aerial Vehicles (UAVs) for investigating the direct effects of floods, even in the harsh Arctic environment.

This Web Map is included in the Mitigating Marshes Against Sea Level Rise: Thin Layer Placement Experiment application.The National Estuarine Research Reserve (NERR) System Science Collaborative funded a two-year experiment at 8 different NERR sites to provide broad geographic scale, including Chesapeake Bay NERR in Virginia. The three core research questions they aim to answer include: “Is sediment addition an effective adaptation strategy for marshes in the face of sea level rise? How does marsh resilience respond to different levels of sediment addition? How do low versus high marsh habitats differ in their response to this restoration strategy?”.This Story Map is a tool for 6th-12th grade teachers to help teach students about marshes and thin layer placement restoration techniques by exploring maps, videos, and images. Students will analyze how vegetation has changed in the Chesapeake Bay National Estuarine Research Reserve in Virginia (CBNERR-VA) marsh experiment plots in the first year of monitoring. They will evaluate images and graphs different treatments and determine which could be used as a possible restoration technique to combat sea level rise in marshes.Data: https://www.vims.edu/cbnerr/resources/gis-data-layers/index.php

Drone products captured by CDFW staff for Hope Valley Wildlife Area.

This dataset contains data used to test the protocol for high-resolution mapping and monitoring of recreational impacts in protected natural areas (PNAs) using unmanned aerial vehicle (UAV) surveys, Structure-from-Motion (SfM) data processing and geographic information systems (GIS) analysis to derive spatially coherent information about trail conditions (Tomczyk et al., 2023). Dataset includes the following folders:

Cocora_raster_data (~3GB) and Vinicunca_raster_data (~32GB) - a very high-resolution (cm-scale) dataset derived from UAV-generated images. Data covers selected recreational trails in Colombia (Valle de Cocora) and Peru (Vinicunca). UAV-captured images were processed using the structure-from-motion approach in Agisoft Metashape software. Data are available as GeoTIFF files in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru). Individual files are named as follows [location]_[year]_[product]_[raster cell size].tif, where:

[location] is the place of data collection (e.g., Cocora, Vinicucna)

[year] is the year of data collection (e.g., 2023)

[product] is the tape of files: DEM = digital elevation model; ortho = orthomosaic; hs = hillshade

[raster cell size] is the dimension of individual raster cell in mm (e.g., 15mm)

Cocora_vector_data. and Vinicunca_vector_data – mapping of trail tread and conditions in GIS environment (ArcPro). Data are available as shp files. Data are in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru).

Structure-from-motio n processing was performed in Agisoft Metashape (https://www.agisoft.com/, Agisoft, 2023). Mapping was performed in ArcGIS Pro (https://www.esri.com/en-us/arcgis/about-arcgis/overview, Esri, 2022). Data can be used in any GIS software, including commercial (e.g. ArcGIS) or open source (e.g. QGIS).

Tomczyk, A. M., Ewertowski, M. W., Creany, N., Monz, C. A., & Ancin-Murguzur, F. J. (2023). The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions. International Journal of Applied Earth Observations and Geoinformation, 103474. doi: https://doi.org/10.1016/j.jag.2023.103474

Hilltop Arboretum Dataset for GRASS GIS

This geospatial dataset contains raster data for the landform at Hilltop Arboretum, Baton Rouge, Louisiana, USA. This data was collected in an aerial survey with a DJI Phantom 4 Pro drone over Hilltop Arboretum on 12/31/2019 by Brendan Harmon and Josef Horacek. The aerial photographs were processed in Agisoft Metashape using Structure from Motion (SfM) to generate a point cloud, orthophotograph, and digital surface model. The point cloud was processed in CloudCompare to generate a bare earth point cloud. The orthophoto, digital surface model, and bare earth point cloud were imported into GRASS GIS. The bare earth point cloud was interpolated as a digital elevation model using the Regularized Spline with Tension method. The top level directory lousiana_s_spm_hilltop is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Meters with EPSG code 26982. Inside the location there are the PERMANENT mapset, a license file, and readme file.

Survey

• Location: LSU Hilltop Arboretum, Baton Rouge, Louisiana, USA.
• Drone: DJI Phantom 4 Pro
• Software: Agisoft Metashape, CloudCompare, GRASS GIS
• Team: Brendan Harmon and Josef Horacek
• Date: 12/31/2019
• GCPs: 10 AeroPoints

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

GIS2DJI is a Python 3 program created to exports GIS files to a simple kml compatible with DJI pilot. The software is provided with a GUI. GIS2DJI has been tested with the following file formats: gpkg, shp, mif, tab, geojson, gml, kml and kmz. GIS_2_DJI will scan every file, every layer and every geometry collection (ie: MultiPoints) and create one output kml or kmz for each object found. It will import points, lines and polygons, and converted each object into a compatible DJI kml file. Lines and polygons will be exported as kml files. Points will be converted as PseudoPoints.kml. A PseudoPoints fools DJI to import a point as it thinks it's a line with 0 length. This allows you to import points in mapping missions. Points will also be exported as Point.kmz because PseudoPoints are not visible in a GIS or in Google Earth. The .kmz file format should make points compatible with some DJI mission software.

Human life is precious and in the event of any unfortunate occurrence, highest efforts are made to safeguard it. To provide timely aid or undertake extraction of humans in distress, it is critical to accurately locate them. There has been an increased usage of drones to detect and track humans in such situations. Drones are used to capture high resolution images during search and rescue purposes. It is possible to find survivors from drone feed, but that requires manual analysis. This is a time taking process and is prone to human errors. This model can detect humans by looking at drone imagery and can draw bounding boxes around the location. This model is trained on IPSAR and SARD datasets where humans are on macadam roads, in quarries, low and high grass, forest shade, and Mediterranean and Sub-Mediterranean landscapes. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of detection, reducing the time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution (1-5 cm) individual drone images or an orthomosaic.OutputFeature class containing detected humans.Applicable geographiesThe model is expected to work well in Mediterranean and Sub-Mediterranean landscapes but can also be tried in other areas.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 82.2 percent for human class.Training dataThis model is trained on search and rescue dataset provided by IPSAR and SARD.LimitationsThis model has a tendency to maximize detection of humans and errors towards producing false positives in rocky areas.Sample resultsHere are a few results from the model.

NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of June 2018 is based on NPM Baseline Assessment 11 (as of 14 June) and NPM most updated drone imagery (as of 21 June).

Here below, the complete package by camp:

• SW Map package
• KMZ file
• Drone image

The full image and shapefiles are available at this link.

Flight paths of drone surveys used to capture imagery for the July 6, 2021, Saint Joachim, ON downburst. Ground survey conducted July 7, 2021. DJI Air 2S performed 3 flights. Please note that drones are also used for scouting the initial area of interest using a live view on the controller, meaning that some flight paths may not be associated with any imagery. Does not include flights where drone mapping was performed.View event map here

The global Digital Mapping Cameras (DMC) market is experiencing steady growth, projected to reach $230.5 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 3.2% from 2025 to 2033. This growth is fueled by increasing demand for high-resolution imagery across various sectors, including surveying, mapping, agriculture, and infrastructure development. The rising adoption of unmanned aircraft systems (UAS) or drones for aerial photography significantly contributes to market expansion, as they offer cost-effective and efficient data acquisition compared to traditional manned aircraft methods. Technological advancements, such as improved sensor technologies and enhanced image processing capabilities, further drive market expansion by enabling more accurate and detailed mapping solutions. Market segmentation reveals a strong preference for linear array scanners (pushbroom) due to their ability to capture high-quality imagery quickly and efficiently. The application of DMCs in manned aircraft remains significant, although the UAS segment is expected to witness faster growth due to its flexibility and lower operational costs. Competition within the market is robust, with established players such as Vexcel Imaging, Leica Geosystems, and Teledyne Optech alongside newer entrants continually innovating to enhance product offerings and cater to diverse customer needs. The North American market currently holds a dominant share, driven by robust technological advancements and substantial investments in infrastructure projects. However, the Asia-Pacific region is poised for significant growth in the coming years, fueled by rapid urbanization, infrastructure development, and increasing adoption of advanced mapping technologies. While factors like the high initial investment costs of DMCs and potential regulatory hurdles related to drone usage could act as restraints, the overall market outlook for digital mapping cameras remains positive, indicating considerable potential for growth and innovation over the forecast period. The market's evolution will likely see an increased emphasis on data analytics capabilities integrated with DMCs, enabling users to derive actionable insights from the acquired imagery, expanding the application scope beyond basic mapping and into areas like precision agriculture and environmental monitoring.

Multi-page experience builder site. One page using journey template with bookmarks to identify vertical drone ortho capture locations. One page using journey template with bookmarks to identify drone captured 3-D model locations. One page StoryMap description of Woburn's drone program.

Airborne platforms used for testing the app and the various sites where flight tests were performed.

NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of March 2019 is based on NPM Site Assessment 14 (as of 13 February 2019) and NPM UAV imagery (as of 23 January 2019).

Here below, the complete package by camp:

SW Map package KMZ file Drone image

The full image and shapefiles are available at this link.

Flight paths of drone surveys used to capture imagery and video for the July 24, 2019, Hanmore Lake, AB tornado. Ground survey conducted July 26, 2019. DJI Mavic 2 Pro performed 2 flights. Please note that drones are also used for scouting the initial area of interest using a live view on the controller, meaning that some flight paths may not be associated with any imagery. Does not include flights where drone mapping was conducted. View event map here

Example metadata records from the two handsets tested in the study.

Flight paths of drone surveys used to capture imagery and video for the July 20, 2023 South Buxton, ON tornado. Ground survey conducted July 21, 2023. DJI Mavic 3E performed 4 flights. Please note that drones are also used for scouting the initial area of interest using a live view on the controller, meaning that some flight paths may not be associated with any imagery.View Event Summary Map Here

Flight paths of drone surveys used to capture imagery and video for the June 5, 2023, Peerless, SK downburst. Ground survey conducted June 6 & 7, 2023. DJI Mavic 2 Pro performed 3 flights. Please note that drones are also used for scouting the initial area of interest using a live view on the controller, meaning that some flight paths may not be associated with any imagery.

Creation 18 march 2015, that example take the regulation of the FAA rulesRevised at each 6 month for the Aeronautical dataNo drone near: Airport large medium small, heliport, seaplane, Air Space Risk, Class B C D, NEW LAYERS:Nuclear Power, Prisons, World Urban add Near-Real-Time Surface In-Sit from NOAA for the Weather and Wind!TRY THAT ONE (ICAO app with App Builder): Where you can fly drones!Get a better understanding of where you can fly drones!Study about the DRONE where we have the right to fly and it will be safe...Waiting for clarification and rules...If you go to http://gis.icao.int/drone You have the nearby function very cool to fly your DRONE more safety...The Drone fly tools APP work on all Device an is simple to useCreation 18 march 2015, Get a better understanding of where you can fly drones! Try the Widget SUASLP RISK Special Use Airspace, USE SPACIAL FILTER: Select spacial filter, user defined area to select the zone where you want to fly; click to apply and see the result of areas that you don't have the right to go! Try Class B C D tool widget, Try Class A B C D E F G tool widget and Class G tool widget. Other widget like Query No Drone near Airports... Study about the DRONE where we have the right to fly and it will be safe...Waiting for clarification and rules...Get a better understanding of where you can fly drones! 9631 Airports with 5 miles buffer, 46325 Airports with 5 miles buffer from ourports.com, SUAS lower with all CLASS, many other layers like: ROUTES, CTA, CTR, SCTR, World Wind from NOAA, RESTRICTED AREA and more...working on Heliport databaseOther one (ICAO app with App Builder): Where you can fly drones! More tools!

This map shows locations where STRI drones have flown. For each of these polygons, we have orthophotos, digital surface model (DSM) and cloud points. At the Smithsonian Tropical Research Institute (STRI) Panama, we have DJI Phantom 4 pro, Sense Fly eBee, 3DR Solo among other drones ready to flight, depending of the area, coverage and parts availability.If you need access to any of the byproducts, please, send us an email requesting the data.